OpenRaiser/Intern-Atlas huggingface dataset:
The Effectiveness of Traditional Tools and Computer-Aided Technologies for Health and Safety Training in the Construction Sector: A Systematic Review
Venue: arXiv cs.CY 2018
Year: 2018

For workers, the exposure to on-site hazards can result in fatalities and serious injuries. To improve safety outcomes, different approaches have been implemented for health and safety training in the construction sector, such as traditional tools and computer-aided technologies (e.g., serious games and virtual reality). However, the effectiveness of these approaches has been barely explored. In order to bridge this gap, a systematic review of existing studies was conducted. Unlike previous review studies in this field that focused on uncovering the technology characters and challenges, this study mainly evaluated the effectiveness of training using traditional tools and computer-aided technologies on the well-being of individuals. Measures of the effectiveness included knowledge acquisition, unsafe behaviour alteration, and injury rate reduction. Results indicated that: 1. the effectiveness of traditional tools is sufficiently supported by statistical evidence; and 2. the use of computer-aided technologies has evidence to support its effectiveness, but more solid evidence is required to support this statement. It was also found that the overall performance of computer-aided technologies is superior in several technical aspects compared to traditional tools, namely, representing actual workplace situations, providing text-free interfaces, having better user engagement, and being more cost-efficient. Finally, using the systematic review findings, a theoretical framework is proposed as a potential solution to help future research in this field systematically examine the effectiveness and usability of their approaches. This framework is theoretical in nature and requires further validation. A further study is therefore proposed to test and validate this framework.
The Internals of the Data Calculator
Venue: arXiv cs.DB 2018
Year: 2018

Data structures are critical in any data-driven scenario, but they are notoriously hard to design due to a massive design space and the dependence of performance on workload and hardware which evolve continuously. We present a design engine, the Data Calculator, which enables interactive and semi-automated design of data structures. It brings two innovations. First, it offers a set of fine-grained design primitives that capture the first principles of data layout design: how data structure nodes lay data out, and how they are positioned relative to each other. This allows for a structured description of the universe of possible data structure designs that can be synthesized as combinations of those primitives. The second innovation is computation of performance using learned cost models. These models are trained on diverse hardware and data profiles and capture the cost properties of fundamental data access primitives (e.g., random access). With these models, we synthesize the performance cost of complex operations on arbitrary data structure designs without having to: 1) implement the data structure, 2) run the workload, or even 3) access the target hardware. We demonstrate that the Data Calculator can assist data structure designers and researchers by accurately answering rich what-if design questions on the order of a few seconds or minutes, i.e., computing how the performance (response time) of a given data structure design is impacted by variations in the: 1) design, 2) hardware, 3) data, and 4) query workloads. This makes it effortless to test numerous designs and ideas before embarking on lengthy implementation, deployment, and hardware acquisition steps. We also demonstrate that the Data Calculator can synthesize entirely new designs, auto-complete partial designs, and detect suboptimal design choices.
Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Space
Venue: arXiv cs.DM 2018
Year: 2018

This paper focuses on devising graph signal processing tools for the treatment of data defined on the edges of a graph. We first show that conventional tools from graph signal processing may not be suitable for the analysis of such signals. More specifically, we discuss how the underlying notion of a `smooth signal' inherited from (the typically considered variants of) the graph Laplacian are not suitable when dealing with edge signals that encode a notion of flow. To overcome this limitation we introduce a class of filters based on the Edge-Laplacian, a special case of the Hodge-Laplacian for simplicial complexes of order one. We demonstrate how this Edge-Laplacian leads to low-pass filters that enforce (approximate) flow-conservation in the processed signals. Moreover, we show how these new filters can be combined with more classical Laplacian-based processing methods on the line-graph. Finally, we illustrate the developed tools by denoising synthetic traffic flows on the London street network.
Piping Botnet - Turning Green Technology into a Water Disaster
Venue: arXiv cs.CR 2018
Year: 2018

The current generation of IoT devices is being used by clients and consumers to regulate resources (such as water and electricity) obtained from critical infrastructure (such as urban water services and smart grids), creating a new attack vector against critical infrastructure. In this research we show that smart irrigation systems, a new type of green technology and IoT device aimed at saving water and money, can be used by attackers as a means of attacking urban water services. We present a distributed attack model that can be used by an attacker to attack urban water services using a botnet of commercial smart irrigation systems. Then, we show how a bot running on a compromised device in a LAN can:(1) detect a connected commercial smart irrigation system (RainMachine, BlueSpray, and GreenIQ) within 15 minutes by analyzing LAN's behavior using a dedicated classification model, and (2) launch watering via a commercial smart irrigation system according to an attacker's wishes using spoofing and replay attacks. In addition, we model the damage that can be caused by performing such an attack and show that a standard water tower can be emptied in an hour using a botnet of 1,355 sprinklers and a flood water reservoir can be emptied overnight using a botnet of 23,866 sprinklers. Finally, we discuss countermeasure methods and hypothesize whether the next generation of plumbers will use Kali Linux instead of a monkey wrench.
EOE: Expected Overlap Estimation over Unstructured Point Cloud Data
Venue: arXiv cs.CV 2018
Year: 2018

We present an iterative overlap estimation technique to augment existing point cloud registration algorithms that can achieve high performance in difficult real-world situations where large pose displacement and non-overlapping geometry would otherwise cause traditional methods to fail. Our approach estimates overlapping regions through an iterative Expectation Maximization procedure that encodes the sensor field-of-view into the registration process. The proposed technique, Expected Overlap Estimation (EOE), is derived from the observation that differences in field-of-view violate the iid assumption implicitly held by all maximum likelihood based registration techniques. We demonstrate how our approach can augment many popular registration methods with minimal computational overhead. Through experimentation on both synthetic and real-world datasets, we find that adding an explicit overlap estimation step can aid robust outlier handling and increase the accuracy of both ICP-based and GMM-based registration methods, especially in large unstructured domains and where the amount of overlap between point clouds is very small.
Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers
Venue: arXiv cs.IT 2018
Year: 2018

Distributed gradient descent (DGD) is an efficient way of implementing gradient descent (GD), especially for large data sets, by dividing the computation tasks into smaller subtasks and assigning to different computing servers (CSs) to be executed in parallel. In standard parallel execution, per-iteration waiting time is limited by the execution time of the straggling servers. Coded DGD techniques have been introduced recently, which can tolerate straggling servers via assigning redundant computation tasks to the CSs. In most of the existing DGD schemes, either with coded computation or coded communication, the non-straggling CSs transmit one message per iteration once they complete all their assigned computation tasks. However, although the straggling servers cannot complete all their assigned tasks, they are often able to complete a certain portion of them. In this paper, we allow multiple transmissions from each CS at each iteration in order to make sure a maximum number of completed computations can be reported to the aggregating server (AS), including the straggling servers. We numerically show that the average completion time per iteration can be reduced significantly by slightly increasing the communication load per server.
A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras
Venue: arXiv cs.CV 2018
Year: 2018

Light field cameras can capture both spatial and angular information of light rays, enabling 3D reconstruction by a single exposure. The geometry of 3D reconstruction is affected by intrinsic parameters of a light field camera significantly. In the paper, we propose a multi-projection-center (MPC) model with 6 intrinsic parameters to characterize light field cameras based on traditional two-parallel-plane (TPP) representation. The MPC model can generally parameterize light field in different imaging formations, including conventional and focused light field cameras. By the constraints of 4D ray and 3D geometry, a 3D projective transformation is deduced to describe the relationship between geometric structure and the MPC coordinates. Based on the MPC model and projective transformation, we propose a calibration algorithm to verify our light field camera model. Our calibration method includes a close-form solution and a non-linear optimization by minimizing re-projection errors. Experimental results on both simulated and real scene data have verified the performance of our algorithm.
Universal Perceptual Grouping
Venue: arXiv cs.CV 2018
Year: 2018

In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping (SPG) dataset to date, consisting of 20,000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep universal perceptual grouping model. The model is learned with both generative and discriminative losses. The generative losses improve the generalisation ability of the model to unseen object categories and datasets. The discriminative losses include a local grouping loss and a novel global grouping loss to enforce global grouping consistency. We show that the proposed model significantly outperforms the state-of-the-art groupers. Further, we show that our grouper is useful for a number of sketch analysis tasks including sketch synthesis and fine-grained sketch-based image retrieval (FG-SBIR).
Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions
Venue: arXiv cs.NA 2018
Year: 2018

This work is devoted to elaboration on the idea to use block term decomposition for group data analysis and to raise the possibility of modelling group activity with (Lr, 1) and Tucker blocks. A new generalization of block tensor decomposition was considered in application to group data analysis. Suggested approach was evaluated on multilabel classification task for a set of images. This contribution also reports results of investigation on clustering with proposed tensor models in comparison with known matrix models, namely common orthogonal basis extraction and group independent component analysis.
Capturing global spatial context for accurate cell classification in skin cancer histology
Venue: arXiv cs.CV 2018
Year: 2018

The spectacular response observed in clinical trials of immunotherapy in patients with previously uncurable Melanoma, a highly aggressive form of skin cancer, calls for a better understanding of the cancer-immune interface. Computational pathology provides a unique opportunity to spatially dissect such interface on digitised pathological slides. Accurate cellular classification is a key to ensure meaningful results, but is often challenging even with state-of-art machine learning and deep learning methods. We propose a hierarchical framework, which mirrors the way pathologists perceive tumour architecture and define tumour heterogeneity to improve cell classification methods that rely solely on cell nuclei morphology. The SLIC superpixel algorithm was used to segment and classify tumour regions in low resolution H&E-stained histological images of melanoma skin cancer to provide a global context. Classification of superpixels into tumour, stroma, epidermis and lumen/white space, yielded a 97.7% training set accuracy and 95.7% testing set accuracy in 58 whole-tumour images of the TCGA melanoma dataset. The superpixel classification was projected down to high resolution images to enhance the performance of a single cell classifier, based on cell nuclear morphological features, and resulted in increasing its accuracy from 86.4% to 91.6%. Furthermore, a voting scheme was proposed to use global context as biological a priori knowledge, pushing the accuracy further to 92.8%. This study demonstrates how using the global spatial context can accurately characterise the tumour microenvironment and allow us to extend significantly beyond single-cell morphological classification.
Optimal voltage control using singular value decomposition of fast decoupled load flow jacobian
Venue: arXiv cs.SY 2018
Year: 2018

The problem of regulating voltages within the required limits is complicated by the fact that power system supplies power to a vast number of loads and is fed from many generating units. As loads vary, reactive power requirements of the transmission system vary. Moreover, voltage magnitude is relatively less sensitive to active power compared to reactive power due to high X/R ratio of transmission lines. Therefore, separating voltage control from active power is not only justified but also the common and practical way in power transmission systems. Considering these facts, the fast decoupled power flow jacobian can be used to control voltage magnitudes by reactive power compensation. In this paper, an optimal voltage control is presented to obtain new voltage set-points for PV buses by maximizing the effect of input change on output change using the Fast Decoupled Load Flow (FDLF) jacobian matrix. The proposed algorithm was tested on three IEEE systems: 9 bus, 14 bus and 30 bus systems.
Control of Multi-Agent Systems with Finite Time Control Barrier Certificates and Temporal Logic
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, a method to synthesize controllers using finite time convergence control barrier functions guided by linear temporal logic specifications for continuous time multi-agent dynamical systems is proposed. Finite time convergence to a desired set in the state space is guaranteed under the existence of a suitable finite time convergence control barrier function. In addition, these barrier functions also guarantee forward invariance once the system converges to the desired set. This allows us to formulate a theoretical framework which synthesizes controllers for the multi-agent system. These properties also enable us to solve the reachability problem in continuous time by formulating a theorem on the composition of multiple finite time convergence control barrier functions. This approach is more flexible than existing methods and also allows for a greater set of feasible control laws. Linear temporal logic is used to specify complex task specifications that need to be satisfied by the multi-agent system. With this solution methodology, a control law is synthesized that satisfies the given temporal logic task specification. Robotic experiments are provided which were performed on the Robotarium multi-robot testbed at Georgia Tech.
Spinal Cord Gray Matter-White Matter Segmentation on Magnetic Resonance AMIRA Images with MD-GRU
Venue: arXiv cs.CV 2018
Year: 2018

The small butterfly shaped structure of spinal cord (SC) gray matter (GM) is challenging to image and to delinate from its surrounding white matter (WM). Segmenting GM is up to a point a trade-off between accuracy and precision. We propose a new pipeline for GM-WM magnetic resonance (MR) image acquisition and segmentation. We report superior results as compared to the ones recently reported in the SC GM segmentation challenge and show even better results using the averaged magnetization inversion recovery acquisitions (AMIRA) sequence. Scan-rescan experiments with the AMIRA sequence show high reproducibility in terms of Dice coefficient, Hausdorff distance and relative standard deviation. We use a recurrent neural network (RNN) with multi-dimensional gated recurrent units (MD-GRU) to train segmentation models on the AMIRA dataset of 855 slices. We added a generalized dice loss to the cross entropy loss that MD-GRU uses and were able to improve the results.
Adapting website design for people with color-blindness
Venue: arXiv cs.HC 2018
Year: 2018

The aim of the study is the description of problem of developing web design for people with color blindness. The objectives of the study are familiarising with the exiting algorithms of simulation color blindness and searching the most appropriate color models to realize a filter of disputed colors. The object of the study is the convertation of color models and algorithms of filtration. The subject of the study are methods of recognition disputed colors. In the study were investigated the problems of color blind people, examined the basic concepts of trichromatic color vision theory, substantiated the necessity of changing different types of color models, given formulas convertation from RGB-color model to HSL-color model, systematized the algorithms of imitation and filtration of colors for different types of dichromacy: protanopia, deuteranopia and tritanopia. The results of the study are planned using in development of adapting website design for people with color blindness.
Learning to Write Notes in Electronic Health Records
Venue: arXiv cs.CL 2018
Year: 2018

Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, reducing time spent with patients and contributing to clinician burnout. With the aspiration of AI-assisted note-writing, we propose a new language modeling task predicting the content of notes conditioned on past data from a patient's medical record, including patient demographics, labs, medications, and past notes. We train generative models using the public, de-identified MIMIC-III dataset and compare generated notes with those in the dataset on multiple measures. We find that much of the content can be predicted, and that many common templates found in notes can be learned. We discuss how such models can be useful in supporting assistive note-writing features such as error-detection and auto-complete.
Training Compact Neural Networks with Binary Weights and Low Precision Activations
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose to train a network with binary weights and low-bitwidth activations, designed especially for mobile devices with limited power consumption. Most previous works on quantizing CNNs uncritically assume the same architecture, though with reduced precision. However, we take the view that for best performance it is possible (and even likely) that a different architecture may be better suited to dealing with low precision weights and activations. Specifically, we propose a "network expansion" strategy in which we aggregate a set of homogeneous low-precision branches to implicitly reconstruct the full-precision intermediate feature maps. Moreover, we also propose a group-wise feature approximation strategy which is very flexible and highly accurate. Experiments on ImageNet classification tasks demonstrate the superior performance of the proposed model, named Group-Net, over various popular architectures. In particular, with binary weights and activations, we outperform the previous best binary neural network in terms of accuracy as well as saving more than 5 times computational complexity on ImageNet with ResNet-18 and ResNet-50.
Accelerating wave-propagation algorithms with adaptive mesh refinement using the Graphics Processing Unit (GPU)
Venue: arXiv cs.MS 2018
Year: 2018

Clawpack is a library for solving nonlinear hyperbolic partial differential equations using high-resolution finite volume methods based on Riemann solvers and limiters. It supports Adaptive Mesh Refinement (AMR), which is essential in solving multi-scale problems. Recently, we added capabilities to accelerate the code by using the Graphics Process Unit (GPU). Routines that manage CPU and GPU AMR data and facilitate the execution of GPU kernels are added. Customized and CPU thread-safe memory managers are designed to manage GPU and CPU memory pools, which is essential in eliminating the overhead of memory allocation and de-allocation. A global reduction is conducted every time step for dynamically adjusting the time step based on Courant number restrictions. Some small GPU kernels are merged into bigger kernels, which greatly reduces kernel launching overhead. A speed-up between $2$ and $3$ for the total running time is observed in an acoustics benchmark problem.
Characterization of infinite LSP words and endomorphisms preserving the LSP property
Venue: arXiv cs.DM 2018
Year: 2018

Answering a question of G. Fici, we give an $S$-adic characterization of thefamily of infinite LSP words, that is, the family of infinite words having all their left special factors as prefixes.More precisely we provide a finite set of morphisms $S$ and an automaton ${\cal A}$ such that an infinite word is LSP if and only if it is $S$-adic and one of its directive words is recognizable by ${\cal A}$.Then we characterize the endomorphisms that preserve the property of being LSP for infinite words.This allows us to prove that there exists no set $S'$ of endomorphisms for which the set of infinite LSP words corresponds to the set of $S'$-adic words. This implies that an automaton is required no matter which set of morphisms is used.
It Takes Two to #MeToo - Using Enclaves to Build Autonomous Trusted Systems
Venue: arXiv cs.CR 2018
Year: 2018

We provide enhanced security against insider attacks in services that manage extremely sensitive data. One example is a #MeToo use case where sexual harassment complaints are reported but only revealed when another complaint is filed against the same perpetrator. Such a service places tremendous trust on service operators which our work aims to relieve. To this end we introduce a new autonomous data management concept which transfers responsibility for the sensitive data from administrators to secure and verifiable hardware. The main idea is to manage all data access via a cluster of autonomous computation agents running inside Intel SGX enclaves. These EConfidante agents share a secret data key which is unknown to any external entity, including the data service administrators, thus eliminating many opportunities for data exposure. In this paper we describe a detailed design of the EConfidante system, its flow and how it is managed and implemented. Our #MeToo design also uses an immutable distributed ledger which is built using components from a Blockchain framework. We implemented a proof of concept of our system for the #MeToo use case and analyze its security properties and implementation details.